[battery_glasses]

When I see stuff like this sometimes I get depressed that I'm building apps that just move little bits of information around the web and don't have any real demonstrable value to the world.

How hard is the jump into the realm of computer vision/AI/robotics for someone with strong skills and experience building web applications? Is the coding much different? Is a company like this employing math/theroy-heavy people to develop new types of AI or are they working higher up the stack and just need people who can pipe data around?

[csomar]

> How hard is the jump into the realm of computer vision/AI/robotics for someone with strong skills and experience building web applications?

If you are willing to make the jump. It's possible. You have an advantage.

> Is the coding much different?

Not as much as they have marketed you to believe.

> Is a company like this employing math/theroy-heavy people to develop new types of AI or are they working higher up the stack and just need people who can pipe data around?

No. They are not making academic thesis. You'd be surprised how primitive and "basic" many of these companies are (comparing to their marketing videos, which are click/eye baits). You can definitively make a difference there.

Of course, as other comments have mentioned, these companies still need front-end, databases, backend, and you know, normal basic automation scripts like everyone else.

[rubicon33]

I would second this with some anecdata...

I have worked for one of these companies that produces fancy AI based hardware.

Turns out the hardware is made in China and the AI is handled by third parties (amazon, etc.)

Does that mean putting it all together is trivial? Of course not. But it's not as difficult as it might seem from the outside.

[thebruce87m]

I work in the embedded field, and have literally done the same application as the video.

We have a mixed team, including mechanical engineers, web developers, electronics guys, embedded software guys.

We do have ML people for custom networks but most of it is object detection/ classification which is a solved path and just needs data management.

We’ve done lots of cool ML applications, but many of them need management systems too so that’s where web apps come in.

You could definitely fill a position in a small company like mine and upskill / sideskill in time.

We’re based in Scotland but do have remote workers in other countries.

[amacneil]

It's a common misconception that you need a ML or robotics PhD to work in the field. There are plenty of frontend & backend web engineering, data engineering, and infrastructure/devops roles available at robotics companies - you don't need to be a domain expert.

For example, at Foxglove[0] we are building open source web-based visualization and data management for robotics (shameless plug: currently hiring).

[0] https://foxglove.dev/

[pabs3]

You might want to post your open positions on FOSSjobs and or add your company to the wiki:

https://www.fossjobs.net/ https://github.com/fossjobs/fossjobs/wiki/resources

[joshvm]

Robotics is a big field and encompasses a huge range of things. Similarly a lot of computer vision is pretty technical, particularly for robotics applications. Have a look at ROS and play around, you can simulate a lot of stuff. I'd also suggest Nvidia's Jetbot but the chip shortage has killed it.

AI-wise, nowadays model training stacks are practically codeless. Training a state of the art model on custom data usually boils down to changing some parameters in a config file, plus some boilerplate code. Not always that easy of course, but training an image classifier today can be a single line of Python.

It's not so much piping data around but dealing with datasets in any kind of non-trivial problem is where the hard work is. Neural nets are just another tool for most data scientists. For example exploring Kaggle will get you familiar with weird datasets, but actually collecting, arranging for data to be labelled, and then cleaning it is a skill on its own.

That's usually where experience comes in. Hence there are a lot of startups charging frankly obscene amounts of money for data labelling and curation tools.

[AlotOfReading]

Positions do all of the above exist. Just depends on the company and where in the stack you are. There's a lot of math and domain expertise on the controls and sensors sides. Most people in any particular company aren't doing those though. There are lots of jobs available for c/c++/Matlab/Python/js.

If you don't want to change what you're doing or using, most modern robotic platforms have a web interface somewhere in them for monitoring the fleet. Any web developer would find their skills perfectly compatible.

[donkeyd]

I work at a company doing smart stuff with computers (which some would consider AI) that helps with, among others, the energy transition and getting people across the world broadband. Only about 2 out of 20 devs are actually working on the complicated algorithms. The others are working on the stuff you need to build around it to allow people to actually use the algos. On top of the algos is a pretty complicated web app, which takes most of the actual development time.

So you won't actually have to jump into a whole new career in order to contribute to these types of products. You just have to search for a company that works on a challenge you want to contribute to.

[thunfischtoast]

It's very much possible, the range of skills needed to deploy actual ML-driven applications ranges from web developers, data engineers, ML engineers, Ops guys to data scientists, so pick what you like.

Then again: Speaking from experience, most uses cases for ML are not fancy, world-saving new approaches like the one in the video but revolve around quite mundane applications and tasks like "how to make users click on this add", "how much will we sell next month", "can we automate the creation of thix Excel-file" etc., so pick your company carefully

[krosaen]

Creator of Django did an ML project last year and has a lot of good insights - general web / programming know-how is really useful for building custom tooling to manage / observe datasets and things like that.

edit: forgot to include link :)

http://www.holovaty.com/writing/machine-learning-thoughts/

[asp_hornet]

good engineers and good companies both know good engineering skills transcend technology and domain. switching is very much possible.